8f4aaba0e4
In the Python API for Gaussian Mixture Model, predict() and predictSoft() methods should raise an error when the input argument is not an RDD. Author: FlytxtRnD <meethu.mathew@flytxt.com> Closes #6180 from FlytxtRnD/GmmPredictException and squashes the following commits: 4b6aa11 [FlytxtRnD] Raise error if the input to predict()/predictSoft() is not an RDD
279 lines
11 KiB
Python
279 lines
11 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import sys
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import array as pyarray
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if sys.version > '3':
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xrange = range
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from numpy import array
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from pyspark import RDD
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from pyspark import SparkContext
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from pyspark.mllib.common import callMLlibFunc, callJavaFunc, _py2java, _java2py
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from pyspark.mllib.linalg import SparseVector, _convert_to_vector
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from pyspark.mllib.stat.distribution import MultivariateGaussian
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from pyspark.mllib.util import Saveable, Loader, inherit_doc
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__all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel', 'GaussianMixture']
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@inherit_doc
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class KMeansModel(Saveable, Loader):
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"""A clustering model derived from the k-means method.
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>>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4, 2)
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>>> model = KMeans.train(
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... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random",
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... seed=50, initializationSteps=5, epsilon=1e-4)
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>>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0]))
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True
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>>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0]))
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True
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>>> model.k
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2
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>>> model.computeCost(sc.parallelize(data))
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2.0000000000000004
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>>> model = KMeans.train(sc.parallelize(data), 2)
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>>> sparse_data = [
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... SparseVector(3, {1: 1.0}),
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... SparseVector(3, {1: 1.1}),
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... SparseVector(3, {2: 1.0}),
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... SparseVector(3, {2: 1.1})
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... ]
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>>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||",
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... seed=50, initializationSteps=5, epsilon=1e-4)
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>>> model.predict(array([0., 1., 0.])) == model.predict(array([0, 1.1, 0.]))
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True
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>>> model.predict(array([0., 0., 1.])) == model.predict(array([0, 0, 1.1]))
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True
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>>> model.predict(sparse_data[0]) == model.predict(sparse_data[1])
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True
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>>> model.predict(sparse_data[2]) == model.predict(sparse_data[3])
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True
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>>> isinstance(model.clusterCenters, list)
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True
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>>> import os, tempfile
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>>> path = tempfile.mkdtemp()
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>>> model.save(sc, path)
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>>> sameModel = KMeansModel.load(sc, path)
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>>> sameModel.predict(sparse_data[0]) == model.predict(sparse_data[0])
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True
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>>> try:
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... os.removedirs(path)
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... except OSError:
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... pass
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"""
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def __init__(self, centers):
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self.centers = centers
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@property
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def clusterCenters(self):
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"""Get the cluster centers, represented as a list of NumPy arrays."""
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return self.centers
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@property
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def k(self):
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"""Total number of clusters."""
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return len(self.centers)
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def predict(self, x):
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"""Find the cluster to which x belongs in this model."""
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best = 0
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best_distance = float("inf")
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x = _convert_to_vector(x)
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for i in xrange(len(self.centers)):
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distance = x.squared_distance(self.centers[i])
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if distance < best_distance:
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best = i
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best_distance = distance
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return best
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def computeCost(self, rdd):
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"""
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Return the K-means cost (sum of squared distances of points to
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their nearest center) for this model on the given data.
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"""
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cost = callMLlibFunc("computeCostKmeansModel", rdd.map(_convert_to_vector),
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[_convert_to_vector(c) for c in self.centers])
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return cost
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def save(self, sc, path):
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java_centers = _py2java(sc, [_convert_to_vector(c) for c in self.centers])
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java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel(java_centers)
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java_model.save(sc._jsc.sc(), path)
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@classmethod
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def load(cls, sc, path):
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java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel.load(sc._jsc.sc(), path)
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return KMeansModel(_java2py(sc, java_model.clusterCenters()))
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class KMeans(object):
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@classmethod
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def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||",
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seed=None, initializationSteps=5, epsilon=1e-4):
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"""Train a k-means clustering model."""
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model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations,
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runs, initializationMode, seed, initializationSteps, epsilon)
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centers = callJavaFunc(rdd.context, model.clusterCenters)
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return KMeansModel([c.toArray() for c in centers])
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class GaussianMixtureModel(object):
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"""A clustering model derived from the Gaussian Mixture Model method.
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>>> from pyspark.mllib.linalg import Vectors, DenseMatrix
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>>> clusterdata_1 = sc.parallelize(array([-0.1,-0.05,-0.01,-0.1,
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... 0.9,0.8,0.75,0.935,
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... -0.83,-0.68,-0.91,-0.76 ]).reshape(6, 2))
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>>> model = GaussianMixture.train(clusterdata_1, 3, convergenceTol=0.0001,
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... maxIterations=50, seed=10)
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>>> labels = model.predict(clusterdata_1).collect()
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>>> labels[0]==labels[1]
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False
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>>> labels[1]==labels[2]
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True
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>>> labels[4]==labels[5]
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True
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>>> data = array([-5.1971, -2.5359, -3.8220,
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... -5.2211, -5.0602, 4.7118,
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... 6.8989, 3.4592, 4.6322,
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... 5.7048, 4.6567, 5.5026,
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... 4.5605, 5.2043, 6.2734])
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>>> clusterdata_2 = sc.parallelize(data.reshape(5,3))
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>>> model = GaussianMixture.train(clusterdata_2, 2, convergenceTol=0.0001,
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... maxIterations=150, seed=10)
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>>> labels = model.predict(clusterdata_2).collect()
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>>> labels[0]==labels[1]==labels[2]
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True
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>>> labels[3]==labels[4]
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True
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>>> clusterdata_3 = sc.parallelize(data.reshape(15, 1))
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>>> im = GaussianMixtureModel([0.5, 0.5],
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... [MultivariateGaussian(Vectors.dense([-1.0]), DenseMatrix(1, 1, [1.0])),
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... MultivariateGaussian(Vectors.dense([1.0]), DenseMatrix(1, 1, [1.0]))])
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>>> model = GaussianMixture.train(clusterdata_3, 2, initialModel=im)
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"""
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def __init__(self, weights, gaussians):
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self._weights = weights
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self._gaussians = gaussians
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self._k = len(self._weights)
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@property
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def weights(self):
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"""
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Weights for each Gaussian distribution in the mixture, where weights[i] is
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the weight for Gaussian i, and weights.sum == 1.
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"""
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return self._weights
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@property
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def gaussians(self):
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"""
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Array of MultivariateGaussian where gaussians[i] represents
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the Multivariate Gaussian (Normal) Distribution for Gaussian i.
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"""
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return self._gaussians
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@property
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def k(self):
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"""Number of gaussians in mixture."""
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return self._k
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def predict(self, x):
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"""
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Find the cluster to which the points in 'x' has maximum membership
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in this model.
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:param x: RDD of data points.
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:return: cluster_labels. RDD of cluster labels.
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"""
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if isinstance(x, RDD):
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cluster_labels = self.predictSoft(x).map(lambda z: z.index(max(z)))
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return cluster_labels
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else:
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raise TypeError("x should be represented by an RDD, "
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"but got %s." % type(x))
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def predictSoft(self, x):
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"""
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Find the membership of each point in 'x' to all mixture components.
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:param x: RDD of data points.
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:return: membership_matrix. RDD of array of double values.
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"""
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if isinstance(x, RDD):
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means, sigmas = zip(*[(g.mu, g.sigma) for g in self._gaussians])
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membership_matrix = callMLlibFunc("predictSoftGMM", x.map(_convert_to_vector),
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_convert_to_vector(self._weights), means, sigmas)
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return membership_matrix.map(lambda x: pyarray.array('d', x))
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else:
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raise TypeError("x should be represented by an RDD, "
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"but got %s." % type(x))
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class GaussianMixture(object):
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"""
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Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm.
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:param data: RDD of data points
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:param k: Number of components
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:param convergenceTol: Threshold value to check the convergence criteria. Defaults to 1e-3
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:param maxIterations: Number of iterations. Default to 100
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:param seed: Random Seed
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:param initialModel: GaussianMixtureModel for initializing learning
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"""
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@classmethod
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def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None):
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"""Train a Gaussian Mixture clustering model."""
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initialModelWeights = None
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initialModelMu = None
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initialModelSigma = None
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if initialModel is not None:
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if initialModel.k != k:
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raise Exception("Mismatched cluster count, initialModel.k = %s, however k = %s"
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% (initialModel.k, k))
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initialModelWeights = initialModel.weights
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initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)]
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initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)]
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weight, mu, sigma = callMLlibFunc("trainGaussianMixture", rdd.map(_convert_to_vector), k,
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convergenceTol, maxIterations, seed, initialModelWeights,
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initialModelMu, initialModelSigma)
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mvg_obj = [MultivariateGaussian(mu[i], sigma[i]) for i in range(k)]
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return GaussianMixtureModel(weight, mvg_obj)
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def _test():
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import doctest
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globs = globals().copy()
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globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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globs['sc'].stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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